Anytime optimal algorithms in stochastic multi-armed bandits

@InProceedings{pmlr-v48-degenne16,
  title = 	 {Anytime optimal algorithms in stochastic multi-armed bandits},
  author = 	 {Degenne, Rémy and Perchet, Vianney},
  booktitle = 	 {Proceedings of The 33rd International Conference on Machine Learning},
  pages = 	 {1587--1595},
  year = 	 {2016},
  editor = 	 {Balcan, Maria Florina and Weinberger, Kilian Q.},
  volume = 	 {48},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {New York, New York, USA},
  month = 	 {20--22 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v48/degenne16.pdf},
  url = 	 {https://proceedings.mlr.press/v48/degenne16.html},
  abstract = 	 {We introduce an anytime algorithm for stochastic multi-armed bandit with optimal distribution free and distribution dependent bounds (for a specific family of parameters). The performances of this algorithm (as well as another one motivated by the conjectured optimal bound) are evaluated empirically. A similar analysis is provided with full information, to serve as a benchmark.}
}